Machine learning classification with confidence thresholds

ABSTRACT

A machine learning classifier may classify observations into one or more of i categories, and may be configured to: receive test data that includes j observations, each associated with a respective ground truth category, and produce output that provides, for each particular observation of the j observations, a set of i probabilities, one probability for each of the i categories. For each particular confidence threshold in k confidence thresholds, a computing device may: reclassify, into a null category, any of the j observations for which all of the set of i probabilities are less than the particular confidence threshold, and determine a respective precision value and a respective coverage value for a particular category of the i categories. A specific confidence threshold in the k confidence thresholds may be selected, and further observations may be reclassified into the null category in accordance with the specific confidence threshold.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 15/723,002, filed Oct. 2, 2017, which is herebyincorporated by reference in its entirety.

BACKGROUND

A machine learning classifier predicts a category, from a discrete setof i categories, to which an observation belongs. This classification isbased on a training set of j observations for which the ground truthcategory memberships are known. A popular example of a classifier is anemail spam filter that classifies incoming email messages as either spamor not spam. Given a large and diverse enough training set, such aclassifier can operate with high accuracy on new email messages.

SUMMARY

One of the drawbacks to machine learning classifiers is that an end useris not given enough control over the degree of confidence that theclassifier uses when making classifications. For instance, if an emailspam filter classifies a particular incoming email message as having a98% probability of being spam and a 2% probability of not being spam,the classifier is exhibiting a high degree of confidence in itsprediction. On the other hand, if the email spam filter classifies themessage as having a 51% probability of being spam and a 49% probabilityof not being spam, the classifier is exhibiting a low degree ofconfidence in its prediction.

In some situations, the end user may wish to reclassify observationswith predictions that are below a confidence threshold to a “don't know”or “null” category. Observations classified as such can be manuallyreviewed to determine their proper classifications, and the classifiercan be retrained with this new understanding. Further, the efficacy ofthe classifier may be measured in terms of both its accuracy for theobservations it does classify—i.e., those with a confidence of at leastthe confidence threshold, and how many observations are reclassifiedbecause their confidence is below the confidence threshold. After theefficacy of k confidence thresholds is considered in this manner, one ormore of these confidence thresholds may be selected for use with furtherobservations.

Accordingly, a first example embodiment may involve a machine learningclassifier that classifies observations into one or more of icategories, where the machine learning classifier is configured to:receive test data, wherein the test data includes j observations, eachassociated with a respective ground truth category, where the groundtruth categories are from the i categories, and produce output thatprovides, for each particular observation of the j observations, a setof i probabilities, one probability for each of the i categories. Thefirst example embodiment may also involve a computing device containinga processor and memory, where the memory stores k confidence thresholds,and where the processor is configured to execute instructions stored inthe memory to: for each particular confidence threshold in the kconfidence thresholds: reclassify, into a null category that is not oneof the i categories, any of the j observations for which all of the setof i probabilities are less than the particular confidence threshold,and determine, based on the j observations after reclassification andtheir associated sets of i probabilities, a respective precision valuefor a particular category of the i categories and a respective coveragevalue for the particular category. The processor may also be configuredto: based on the k respective precision values and the k respectivecoverage values, select a specific confidence threshold in the kconfidence thresholds; reclassify, into the null category in accordancewith the specific confidence threshold, at least some furtherobservations in further output from the machine learning classifier; andprovide the reclassified further observations with the further output.

A second example embodiment may involve receiving, by a machine learningclassifier that classifies observations into one or more of icategories, test data, where the test data includes j observations, eachassociated with a respective ground truth category, where the groundtruth categories are from the i categories. The second exampleembodiment may also involve producing, by the machine learningclassifier, output that provides, for each particular observation of thej observations, a set of i probabilities, one probability for each ofthe i categories. The second example embodiment may also involveobtaining, by a computing device, k confidence thresholds. The secondexample embodiment may also involve, for each particular confidencethreshold in the k confidence thresholds, the computing device:reclassifying, into a null category that is not one of the i categories,any of the j observations for which all of the set of i probabilitiesare less than the particular confidence threshold, and determining,based on the j observations after reclassification and their associatedsets of i probabilities, a respective precision value for a particularcategory of the i categories and a respective coverage value for theparticular category. The second example embodiment may also involve,possibly based on the k respective precision values and the k respectivecoverage values, selecting a specific confidence threshold in the kconfidence thresholds. The second example embodiment may also involvereclassifying, into the null category in accordance with the specificconfidence threshold, at least some further observations in furtheroutput from the machine learning classifier. The second exampleembodiment may also involve providing the reclassified furtherobservations with the further output.

In a third example embodiment, an article of manufacture may include anon-transitory computer-readable medium, having stored thereon programinstructions that, upon execution by a computing system, cause thecomputing system to perform operations in accordance with the firstand/or second example embodiment.

In a fourth example embodiment, a system may include various means forcarrying out each of the operations of the first and/or second exampleembodiment.

These as well as other embodiments, aspects, advantages, andalternatives will become apparent to those of ordinary skill in the artby reading the following detailed description, with reference whereappropriate to the accompanying drawings. Further, this summary andother descriptions and figures provided herein are intended toillustrate embodiments by way of example only and, as such, thatnumerous variations are possible. For instance, structural elements andprocess steps can be rearranged, combined, distributed, eliminated, orotherwise changed, while remaining within the scope of the embodimentsas claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a schematic drawing of a computing device, inaccordance with example embodiments.

FIG. 2 illustrates a schematic drawing of a server device cluster, inaccordance with example embodiments.

FIG. 3 depicts a remote network management architecture, in accordancewith example embodiments.

FIG. 4 depicts a communication environment involving a remote networkmanagement architecture, in accordance with example embodiments.

FIG. 5A depicts another communication environment involving a remotenetwork management architecture, in accordance with example embodiments.

FIG. 5B is a flow chart, in accordance with example embodiments.

FIG. 6A is a graph that plots classification precision versusclassification coverage, in accordance with example embodiments.

FIG. 6B is a graph that plots confidence threshold values versusobjective function values, in accordance with example embodiments.

FIG. 7 depicts classifications of observations, in accordance withexample embodiments.

FIG. 8A depicts a graphical user interface, in accordance with exampleembodiments.

FIG. 8B depicts another graphical user interface, in accordance withexample embodiments.

FIG. 9 is a flow chart, in accordance with example embodiments.

DETAILED DESCRIPTION

Example methods, devices, and systems are described herein. It should beunderstood that the words “example” and “exemplary” are used herein tomean “serving as an example, instance, or illustration.” Any embodimentor feature described herein as being an “example” or “exemplary” is notnecessarily to be construed as preferred or advantageous over otherembodiments or features unless stated as such. Thus, other embodimentscan be utilized and other changes can be made without departing from thescope of the subject matter presented herein.

Accordingly, the example embodiments described herein are not meant tobe limiting. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations. For example, theseparation of features into “client” and “server” components may occurin a number of ways.

Further, unless context suggests otherwise, the features illustrated ineach of the figures may be used in combination with one another. Thus,the figures should be generally viewed as component aspects of one ormore overall embodiments, with the understanding that not allillustrated features are necessary for each embodiment.

Additionally, any enumeration of elements, blocks, or steps in thisspecification or the claims is for purposes of clarity. Thus, suchenumeration should not be interpreted to require or imply that theseelements, blocks, or steps adhere to a particular arrangement or arecarried out in a particular order.

I. Introduction

A large enterprise is a complex entity with many interrelatedoperations. Some of these are found across the enterprise, such as humanresources (HR), supply chain, information technology (IT), and finance.However, each enterprise also has its own unique operations that provideessential capabilities and/or create competitive advantages.

To support widely-implemented operations, enterprises typically useoff-the-shelf software applications, such as customer relationshipmanagement (CRM) and human capital management (HCM) packages. However,they may also need custom software applications to meet their own uniquerequirements. A large enterprise often has dozens or hundreds of thesecustom software applications. Nonetheless, the advantages provided bythe embodiments herein are not limited to large enterprises and may beapplicable to an enterprise, or any other type of organization, of anysize.

Many such software applications are developed by individual departmentswithin the enterprise. These range from simple spreadsheets tocustom-built software tools and databases. But the proliferation ofsiloed custom software applications has numerous disadvantages. Itnegatively impacts an enterprise's ability to run and grow its business,innovate, and meet regulatory requirements. The enterprise may find itdifficult to integrate, streamline and enhance its operations due tolack of a single system that unifies its subsystems and data.

To efficiently create custom applications, enterprises would benefitfrom a remotely-hosted application platform that eliminates unnecessarydevelopment complexity. The goal of such a platform would be to reducetime-consuming, repetitive application development tasks so thatsoftware engineers and individuals in other roles can focus ondeveloping unique, high-value features.

In order to achieve this goal, the concept of Application Platform as aService (aPaaS) is introduced, to intelligently automate workflowsthroughout the enterprise. An aPaaS system is hosted remotely from theenterprise, but may access data, applications, and services within theenterprise by way of secure connections. Such an aPaaS system may have anumber of advantageous capabilities and characteristics. Theseadvantages and characteristics may be able to improve the enterprise'soperations and workflow for IT, HR, CRM, customer service, applicationdevelopment, and security.

The aPaaS system may support development and execution ofmodel-view-controller (MVC) applications. MVC applications divide theirfunctionality into three interconnected parts (model, view, andcontroller) in order to isolate representations of information from themanner in which the information is presented to the user, therebyallowing for efficient code reuse and parallel development. Theseapplications may be web-based, and offer create, read, update, delete(CRUD) capabilities. This allows new applications to be built on acommon application infrastructure.

The aPaaS system may support standardized application components, suchas a standardized set of widgets for graphical user interface (GUI)development. In this way, applications built using the aPaaS system havea common look and feel. Other software components and modules may bestandardized as well. In some cases, this look and feel can be brandedor skinned with an enterprise's custom logos and/or color schemes.

The aPaaS system may support the ability to configure the behavior ofapplications using metadata. This allows application behaviors to berapidly adapted to meet specific needs. Such an approach reducesdevelopment time and increases flexibility. Further, the aPaaS systemmay support GUI tools that facilitate metadata creation and management,thus reducing errors in the metadata.

The aPaaS system may support clearly-defined interfaces betweenapplications, so that software developers can avoid unwantedinter-application dependencies. Thus, the aPaaS system may implement aservice layer in which persistent state information and other data isstored.

The aPaaS system may support a rich set of integration features so thatthe applications thereon can interact with legacy applications andthird-party applications. For instance, the aPaaS system may support acustom employee-onboarding system that integrates with legacy HR, IT,and accounting systems.

The aPaaS system may support enterprise-grade security. Furthermore,since the aPaaS system may be remotely hosted, it should also utilizesecurity procedures when it interacts with systems in the enterprise orthird-party networks and services hosted outside of the enterprise. Forexample, the aPaaS system may be configured to share data amongst theenterprise and other parties to detect and identify common securitythreats.

Other features, functionality, and advantages of an aPaaS system mayexist. This description is for purpose of example and is not intended tobe limiting.

As an example of the aPaaS development process, a software developer maybe tasked to create a new application using the aPaaS system. First, thedeveloper may define the data model, which specifies the types of datathat the application uses and the relationships therebetween. Then, viaa GUI of the aPaaS system, the developer enters (e.g., uploads) the datamodel. The aPaaS system automatically creates all of the correspondingdatabase tables, fields, and relationships, which can then be accessedvia an object-oriented services layer.

In addition, the aPaaS system can also build a fully-functional MVCapplication with client-side interfaces and server-side CRUD logic. Thisgenerated application may serve as the basis of further development forthe user. Advantageously, the developer does not have to spend a largeamount of time on basic application functionality. Further, since theapplication may be web-based, it can be accessed from anyInternet-enabled client device. Alternatively or additionally, a localcopy of the application may be able to be accessed, for instance, whenInternet service is not available.

The aPaaS system may also support a rich set of pre-definedfunctionality that can be added to applications. These features includesupport for searching, email, templating, workflow design, reporting,analytics, social media, scripting, mobile-friendly output, andcustomized GUIs.

The following embodiments describe architectural and functional aspectsof example aPaaS systems, as well as the features and advantagesthereof.

II. Example Computing Devices and Cloud-Based Computing Environments

FIG. 1 is a simplified block diagram exemplifying a computing device100, illustrating some of the components that could be included in acomputing device arranged to operate in accordance with the embodimentsherein. Computing device 100 could be a client device (e.g., a deviceactively operated by a user), a server device (e.g., a device thatprovides computational services to client devices), or some other typeof computational platform. Some server devices may operate as clientdevices from time to time in order to perform particular operations, andsome client devices may incorporate server features.

In this example, computing device 100 includes processor 102, memory104, network interface 106, and an input/output unit 108, all of whichmay be coupled by a system bus 110 or a similar mechanism. In someembodiments, computing device 100 may include other components and/orperipheral devices (e.g., detachable storage, printers, and so on).

Processor 102 may be one or more of any type of computer processingelement, such as a central processing unit (CPU), a co-processor (e.g.,a mathematics, graphics, or encryption co-processor), a digital signalprocessor (DSP), a network processor, and/or a form of integratedcircuit or controller that performs processor operations. In some cases,processor 102 may be one or more single-core processors. In other cases,processor 102 may be one or more multi-core processors with multipleindependent processing units. Processor 102 may also include registermemory for temporarily storing instructions being executed and relateddata, as well as cache memory for temporarily storing recently-usedinstructions and data.

Memory 104 may be any form of computer-usable memory, including but notlimited to random access memory (RAM), read-only memory (ROM), andnon-volatile memory (e.g., flash memory, hard disk drives, solid statedrives, compact discs (CDs), digital video discs (DVDs), and/or tapestorage). Thus, memory 104 represents both main memory units, as well aslong-term storage. Other types of memory may include biological memory.

Memory 104 may store program instructions and/or data on which programinstructions may operate. By way of example, memory 104 may store theseprogram instructions on a non-transitory, computer-readable medium, suchthat the instructions are executable by processor 102 to carry out anyof the methods, processes, or operations disclosed in this specificationor the accompanying drawings.

As shown in FIG. 1, memory 104 may include firmware 104A, kernel 104B,and/or applications 104C. Firmware 104A may be program code used to bootor otherwise initiate some or all of computing device 100. Kernel 104Bmay be an operating system, including modules for memory management,scheduling and management of processes, input/output, and communication.Kernel 104B may also include device drivers that allow the operatingsystem to communicate with the hardware modules (e.g., memory units,networking interfaces, ports, and busses), of computing device 100.Applications 104C may be one or more user-space software programs, suchas web browsers or email clients, as well as any software libraries usedby these programs. Memory 104 may also store data used by these andother programs and applications.

Network interface 106 may take the form of one or more wirelineinterfaces, such as Ethernet (e.g., Fast Ethernet, Gigabit Ethernet, andso on). Network interface 106 may also support communication over one ormore non-Ethernet media, such as coaxial cables or power lines, or overwide-area media, such as Synchronous Optical Networking (SONET) ordigital subscriber line (DSL) technologies. Network interface 106 mayadditionally take the form of one or more wireless interfaces, such asIEEE 802.11 (Wifi), BLUETOOTH®, global positioning system (GPS), or awide-area wireless interface. However, other forms of physical layerinterfaces and other types of standard or proprietary communicationprotocols may be used over network interface 106. Furthermore, networkinterface 106 may comprise multiple physical interfaces. For instance,some embodiments of computing device 100 may include Ethernet,BLUETOOTH®, and Wifi interfaces.

Input/output unit 108 may facilitate user and peripheral deviceinteraction with example computing device 100. Input/output unit 108 mayinclude one or more types of input devices, such as a keyboard, a mouse,a touch screen, and so on. Similarly, input/output unit 108 may includeone or more types of output devices, such as a screen, monitor, printer,and/or one or more light emitting diodes (LEDs). Additionally oralternatively, computing device 100 may communicate with other devicesusing a universal serial bus (USB) or high-definition multimediainterface (HDMI) port interface, for example.

In some embodiments, one or more instances of computing device 100 maybe deployed to support an aPaaS architecture. The exact physicallocation, connectivity, and configuration of these computing devices maybe unknown and/or unimportant to client devices. Accordingly, thecomputing devices may be referred to as “cloud-based” devices that maybe housed at various remote data center locations.

FIG. 2 depicts a cloud-based server cluster 200 in accordance withexample embodiments. In FIG. 2, operations of a computing device (e.g.,computing device 100) may be distributed between server devices 202,data storage 204, and routers 206, all of which may be connected bylocal cluster network 208. The number of server devices 202, datastorages 204, and routers 206 in server cluster 200 may depend on thecomputing task(s) and/or applications assigned to server cluster 200.

For example, server devices 202 can be configured to perform variouscomputing tasks of computing device 100. Thus, computing tasks can bedistributed among one or more of server devices 202. To the extent thatthese computing tasks can be performed in parallel, such a distributionof tasks may reduce the total time to complete these tasks and return aresult. For purpose of simplicity, both server cluster 200 andindividual server devices 202 may be referred to as a “server device.”This nomenclature should be understood to imply that one or moredistinct server devices, data storage devices, and cluster routers maybe involved in server device operations.

Data storage 204 may be data storage arrays that include drive arraycontrollers configured to manage read and write access to groups of harddisk drives and/or solid state drives. The drive array controllers,alone or in conjunction with server devices 202, may also be configuredto manage backup or redundant copies of the data stored in data storage204 to protect against drive failures or other types of failures thatprevent one or more of server devices 202 from accessing units ofcluster data storage 204. Other types of memory aside from drives may beused.

Routers 206 may include networking equipment configured to provideinternal and external communications for server cluster 200. Forexample, routers 206 may include one or more packet-switching and/orrouting devices (including switches and/or gateways) configured toprovide (i) network communications between server devices 202 and datastorage 204 via cluster network 208, and/or (ii) network communicationsbetween the server cluster 200 and other devices via communication link210 to network 212.

Additionally, the configuration of cluster routers 206 can be based atleast in part on the data communication requirements of server devices202 and data storage 204, the latency and throughput of the localcluster network 208, the latency, throughput, and cost of communicationlink 210, and/or other factors that may contribute to the cost, speed,fault-tolerance, resiliency, efficiency and/or other design goals of thesystem architecture.

As a possible example, data storage 204 may include any form ofdatabase, such as a structured query language (SQL) database. Varioustypes of data structures may store the information in such a database,including but not limited to tables, arrays, lists, trees, and tuples.Furthermore, any databases in data storage 204 may be monolithic ordistributed across multiple physical devices.

Server devices 202 may be configured to transmit data to and receivedata from cluster data storage 204. This transmission and retrieval maytake the form of SQL queries or other types of database queries, and theoutput of such queries, respectively. Additional text, images, video,and/or audio may be included as well. Furthermore, server devices 202may organize the received data into web page representations. Such arepresentation may take the form of a markup language, such as thehypertext markup language (HTML), the extensible markup language (XML),or some other standardized or proprietary format. Moreover, serverdevices 202 may have the capability of executing various types ofcomputerized scripting languages, such as but not limited to Perl,Python, PHP Hypertext Preprocessor (PHP), Active Server Pages (ASP),JavaScript, and so on. Computer program code written in these languagesmay facilitate the providing of web pages to client devices, as well asclient device interaction with the web pages.

III. Example Remote Network Management Architecture

FIG. 3 depicts a remote network management architecture, in accordancewith example embodiments. This architecture includes three maincomponents, managed network 300, remote network management platform 320,and third-party networks 340, all connected by way of Internet 350.

Managed network 300 may be, for example, an enterprise network used by abusiness for computing and communications tasks, as well as storage ofdata. Thus, managed network 300 may include various client devices 302,server devices 304, routers 306, virtual machines 308, firewall 310,and/or proxy servers 312. Client devices 302 may be embodied bycomputing device 100, server devices 304 may be embodied by computingdevice 100 or server cluster 200, and routers 306 may be any type ofrouter, switch, or gateway.

Virtual machines 308 may be embodied by one or more of computing device100 or server cluster 200. In general, a virtual machine is an emulationof a computing system, and mimics the functionality (e.g., processor,memory, and communication resources) of a physical computer. Onephysical computing system, such as server cluster 200, may support up tothousands of individual virtual machines. In some embodiments, virtualmachines 308 may be managed by a centralized server device orapplication that facilitates allocation of physical computing resourcesto individual virtual machines, as well as performance and errorreporting. Enterprises often employ virtual machines in order toallocate computing resources in an efficient, as needed fashion.Providers of virtualized computing systems include VMWARE® andMICROSOFT®.

Firewall 310 may be one or more specialized routers or server devicesthat protect managed network 300 from unauthorized attempts to accessthe devices, applications, and services therein, while allowingauthorized communication that is initiated from managed network 300.Firewall 310 may also provide intrusion detection, web filtering, virusscanning, application-layer gateways, and other applications orservices. In some embodiments not shown in FIG. 3, managed network 300may include one or more virtual private network (VPN) gateways withwhich it communicates with remote network management platform 320 (seebelow).

Managed network 300 may also include one or more proxy servers 312. Anembodiment of proxy servers 312 may be a server device that facilitatescommunication and movement of data between managed network 300, remotenetwork management platform 320, and third-party networks 340. Inparticular, proxy servers 312 may be able to establish and maintainsecure communication sessions with one or more computational instancesof remote network management platform 320. By way of such a session,remote network management platform 320 may be able to discover andmanage aspects of the architecture and configuration of managed network300 and its components. Possibly with the assistance of proxy servers312, remote network management platform 320 may also be able to discoverand manage aspects of third-party networks 340 that are used by managednetwork 300.

Firewalls, such as firewall 310, typically deny all communicationsessions that are incoming by way of Internet 350, unless such a sessionwas ultimately initiated from behind the firewall (i.e., from a deviceon managed network 300) or the firewall has been explicitly configuredto support the session. By placing proxy servers 312 behind firewall 310(e.g., within managed network 300 and protected by firewall 310), proxyservers 312 may be able to initiate these communication sessions throughfirewall 310. Thus, firewall 310 might not have to be specificallyconfigured to support incoming sessions from remote network managementplatform 320, thereby avoiding potential security risks to managednetwork 300.

In some cases, managed network 300 may consist of a few devices and asmall number of networks. In other deployments, managed network 300 mayspan multiple physical locations and include hundreds of networks andhundreds of thousands of devices. Thus, the architecture depicted inFIG. 3 is capable of scaling up or down by orders of magnitude.

Furthermore, depending on the size, architecture, and connectivity ofmanaged network 300, a varying number of proxy servers 312 may bedeployed therein. For example, each one of proxy servers 312 may beresponsible for communicating with remote network management platform320 regarding a portion of managed network 300. Alternatively oradditionally, sets of two or more proxy servers may be assigned to sucha portion of managed network 300 for purposes of load balancing,redundancy, and/or high availability.

Remote network management platform 320 is a hosted environment thatprovides aPaaS services to users, particularly to the operators ofmanaged network 300. These services may take the form of web-basedportals, for instance. Thus, a user can securely access remote networkmanagement platform 320 from, for instance, client devices 302, orpotentially from a client device outside of managed network 300. By wayof the web-based portals, users may design, test, and deployapplications, generate reports, view analytics, and perform other tasks.

As shown in FIG. 3, remote network management platform 320 includes fourcomputational instances 322, 324, 326, and 328. Each of these instancesmay represent a set of web portals, services, and applications (e.g., awholly-functioning aPaaS system) available to a particular customer. Insome cases, a single customer may use multiple computational instances.For example, managed network 300 may be an enterprise customer of remotenetwork management platform 320, and may use computational instances322, 324, and 326. The reason for providing multiple instances to onecustomer is that the customer may wish to independently develop, test,and deploy its applications and services. Thus, computational instance322 may be dedicated to application development related to managednetwork 300, computational instance 324 may be dedicated to testingthese applications, and computational instance 326 may be dedicated tothe live operation of tested applications and services. A computationalinstance may also be referred to as a hosted instance, a remoteinstance, a customer instance, or by some other designation.

The multi-instance architecture of remote network management platform320 is in contrast to conventional multi-tenant architectures, overwhich multi-instance architectures have several advantages. Inmulti-tenant architectures, data from different customers (e.g.,enterprises) are comingled in a single database. While these customers'data are separate from one another, the separation is enforced by thesoftware that operates the single database. As a consequence, a securitybreach in this system may impact all customers' data, creatingadditional risk, especially for entities subject to governmental,healthcare, and/or financial regulation. Furthermore, any databaseoperations that impact one customer will likely impact all customerssharing that database. Thus, if there is an outage due to hardware orsoftware errors, this outage affects all such customers. Likewise, ifthe database is to be upgraded to meet the needs of one customer, itwill be unavailable to all customers during the upgrade process. Often,such maintenance windows will be long, due to the size of the shareddatabase.

In contrast, the multi-instance architecture provides each customer withits own database in a dedicated computing instance. This preventscomingling of customer data, and allows each instance to beindependently managed. For example, when one customer's instanceexperiences an outage due to errors or an upgrade, other computationalinstances are not impacted. Maintenance down time is limited because thedatabase only contains one customer's data. Further, the simpler designof the multi-instance architecture allows redundant copies of eachcustomer database and instance to be deployed in a geographicallydiverse fashion. This facilitates high availability, where the liveversion of the customer's instance can be moved when faults are detectedor maintenance is being performed.

In order to support multiple computational instances in an efficientfashion, remote network management platform 320 may implement aplurality of these instances on a single hardware platform. For example,when the aPaaS system is implemented on a server cluster such as servercluster 200, it may operate a virtual machine that dedicates varyingamounts of computational, storage, and communication resources toinstances. But full virtualization of server cluster 200 might not benecessary, and other mechanisms may be used to separate instances. Insome examples, each instance may have a dedicated account and one ormore dedicated databases on server cluster 200. Alternatively,computational instance 322 may span multiple physical devices.

In some cases, a single server cluster of remote network managementplatform 320 may support multiple independent enterprises. Furthermore,as described below, remote network management platform 320 may includemultiple server clusters deployed in geographically diverse data centersin order to facilitate load balancing, redundancy, and/or highavailability.

Third-party networks 340 may be remote server devices (e.g., a pluralityof server clusters such as server cluster 200) that can be used foroutsourced computational, data storage, communication, and servicehosting operations. These servers may be virtualized (i.e., the serversmay be virtual machines). Examples of third-party networks 340 mayinclude AMAZON WEB SERVICES® and MICROSOFT® Azure. Like remote networkmanagement platform 320, multiple server clusters supporting third-partynetworks 340 may be deployed at geographically diverse locations forpurposes of load balancing, redundancy, and/or high availability.

Managed network 300 may use one or more of third-party networks 340 todeploy applications and services to its clients and customers. Forinstance, if managed network 300 provides online music streamingservices, third-party networks 340 may store the music files and provideweb interface and streaming capabilities. In this way, the enterprise ofmanaged network 300 does not have to build and maintain its own serversfor these operations.

Remote network management platform 320 may include modules thatintegrate with third-party networks 340 to expose virtual machines andmanaged services therein to managed network 300. The modules may allowusers to request virtual resources and provide flexible reporting forthird-party networks 340. In order to establish this functionality, auser from managed network 300 might first establish an account withthird-party networks 340, and request a set of associated resources.Then, the user may enter the account information into the appropriatemodules of remote network management platform 320. These modules maythen automatically discover the manageable resources in the account, andalso provide reports related to usage, performance, and billing.

Internet 350 may represent a portion of the global Internet. However,Internet 350 may alternatively represent a different type of network,such as a private wide-area or local-area packet-switched network.

FIG. 4 further illustrates the communication environment between managednetwork 300 and computational instance 322, and introduces additionalfeatures and alternative embodiments. In FIG. 4, computational instance322 is replicated across data centers 400A and 400B. These data centersmay be geographically distant from one another, perhaps in differentcities or different countries. Each data center includes supportequipment that facilitates communication with managed network 300, aswell as remote users.

In data center 400A, network traffic to and from external devices flowseither through VPN gateway 402A or firewall 404A. VPN gateway 402A maybe peered with VPN gateway 412 of managed network 300 by way of asecurity protocol such as Internet Protocol Security (IPSEC) orTransport Layer Security (TLS). Firewall 404A may be configured to allowaccess from authorized users, such as user 414 and remote user 416, andto deny access to unauthorized users. By way of firewall 404A, theseusers may access computational instance 322, and possibly othercomputational instances. Load balancer 406A may be used to distributetraffic amongst one or more physical or virtual server devices that hostcomputational instance 322. Load balancer 406A may simplify user accessby hiding the internal configuration of data center 400A, (e.g.,computational instance 322) from client devices. For instance, ifcomputational instance 322 includes multiple physical or virtualcomputing devices that share access to multiple databases, load balancer406A may distribute network traffic and processing tasks across thesecomputing devices and databases so that no one computing device ordatabase is significantly busier than the others. In some embodiments,computational instance 322 may include VPN gateway 402A, firewall 404A,and load balancer 406A.

Data center 400B may include its own versions of the components in datacenter 400A. Thus, VPN gateway 402B, firewall 404B, and load balancer406B may perform the same or similar operations as VPN gateway 402A,firewall 404A, and load balancer 406A, respectively. Further, by way ofreal-time or near-real-time database replication and/or otheroperations, computational instance 322 may exist simultaneously in datacenters 400A and 400B.

Data centers 400A and 400B as shown in FIG. 4 may facilitate redundancyand high availability. In the configuration of FIG. 4, data center 400Ais active and data center 400B is passive. Thus, data center 400A isserving all traffic to and from managed network 300, while the versionof computational instance 322 in data center 400B is being updated innear-real-time. Other configurations, such as one in which both datacenters are active, may be supported.

Should data center 400A fail in some fashion or otherwise becomeunavailable to users, data center 400B can take over as the active datacenter. For example, domain name system (DNS) servers that associate adomain name of computational instance 322 with one or more InternetProtocol (IP) addresses of data center 400A may re-associate the domainname with one or more IP addresses of data center 400B. After thisre-association completes (which may take less than one second or severalseconds), users may access computational instance 322 by way of datacenter 400B.

FIG. 4 also illustrates a possible configuration of managed network 300.As noted above, proxy servers 312 and user 414 may access computationalinstance 322 through firewall 310. Proxy servers 312 may also accessconfiguration items 410. In FIG. 4, configuration items 410 may refer toany or all of client devices 302, server devices 304, routers 306, andvirtual machines 308, any applications or services executing thereon, aswell as relationships between devices, applications, and services. Thus,the term “configuration items” may be shorthand for any physical orvirtual device, or any application or service remotely discoverable ormanaged by computational instance 322, or relationships betweendiscovered devices, applications, and services. Configuration items maybe represented in a configuration management database (CMDB) ofcomputational instance 322.

As noted above, VPN gateway 412 may provide a dedicated VPN to VPNgateway 402A. Such a VPN may be helpful when there is a significantamount of traffic between managed network 300 and computational instance322, or security policies otherwise suggest or require use of a VPNbetween these sites. In some embodiments, any device in managed network300 and/or computational instance 322 that directly communicates via theVPN is assigned a public IP address. Other devices in managed network300 and/or computational instance 322 may be assigned private IPaddresses (e.g., IP addresses selected from the 10.0.0.0-10.255.255.255or 192.168.0.0-192.168.255.255 ranges, represented in shorthand assubnets 10.0.0.0/8 and 192.168.0.0/16, respectively).

IV. Example Device, Application, and Service Discovery

In order for remote network management platform 320 to administer thedevices, applications, and services of managed network 300, remotenetwork management platform 320 may first determine what devices arepresent in managed network 300, the configurations and operationalstatuses of these devices, and the applications and services provided bythe devices, and well as the relationships between discovered devices,applications, and services. As noted above, each device, application,service, and relationship may be referred to as a configuration item.The process of defining configuration items within managed network 300is referred to as discovery, and may be facilitated at least in part byproxy servers 312.

For purpose of the embodiments herein, an “application” may refer to oneor more processes, threads, programs, client modules, server modules, orany other software that executes on a device or group of devices. A“service” may refer to a high-level capability provided by multipleapplications executing on one or more devices working in conjunctionwith one another. For example, a high-level web service may involvemultiple web application server threads executing on one device andaccessing information from a database application that executes onanother device.

FIG. 5A provides a logical depiction of how configuration items can bediscovered, as well as how information related to discoveredconfiguration items can be stored. For sake of simplicity, remotenetwork management platform 320, third-party networks 340, and Internet350 are not shown.

In FIG. 5A, CMDB 500 and task list 502 are stored within computationalinstance 322. Computational instance 322 may transmit discovery commandsto proxy servers 312. In response, proxy servers 312 may transmit probesto various devices, applications, and services in managed network 300.These devices, applications, and services may transmit responses toproxy servers 312, and proxy servers 312 may then provide informationregarding discovered configuration items to CMDB 500 for storagetherein. Configuration items stored in CMDB 500 represent theenvironment of managed network 300.

Task list 502 represents a list of activities that proxy servers 312 areto perform on behalf of computational instance 322. As discovery takesplace, task list 502 is populated. Proxy servers 312 repeatedly querytask list 502, obtain the next task therein, and perform this task untiltask list 502 is empty or another stopping condition has been reached.

To facilitate discovery, proxy servers 312 may be configured withinformation regarding one or more subnets in managed network 300 thatare reachable by way of proxy servers 312. For instance, proxy servers312 may be given the IP address range 192.168.0/24 as a subnet. Then,computational instance 322 may store this information in CMDB 500 andplace tasks in task list 502 for discovery of devices at each of theseaddresses.

FIG. 5A also depicts devices, applications, and services in managednetwork 300 as configuration items 504, 506, 508, 510, and 512. As notedabove, these configuration items represent a set of physical and/orvirtual devices (e.g., client devices, server devices, routers, orvirtual machines), applications executing thereon (e.g., web servers,email servers, databases, or storage arrays), relationshipstherebetween, as well as services that involve multiple individualconfiguration items.

Placing the tasks in task list 502 may trigger or otherwise cause proxyservers 312 to begin discovery. Alternatively or additionally, discoverymay be manually triggered or automatically triggered based on triggeringevents (e.g., discovery may automatically begin once per day at aparticular time).

In general, discovery may proceed in four logical phases: scanning,classification, identification, and exploration. Each phase of discoveryinvolves various types of probe messages being transmitted by proxyservers 312 to one or more devices in managed network 300. The responsesto these probes may be received and processed by proxy servers 312, andrepresentations thereof may be transmitted to CMDB 500. Thus, each phasecan result in more configuration items being discovered and stored inCMDB 500.

In the scanning phase, proxy servers 312 may probe each IP address inthe specified range of IP addresses for open Transmission ControlProtocol (TCP) and/or User Datagram Protocol (UDP) ports to determinethe general type of device. The presence of such open ports at an IPaddress may indicate that a particular application is operating on thedevice that is assigned the IP address, which in turn may identify theoperating system used by the device. For example, if TCP port 135 isopen, then the device is likely executing a WINDOWS® operating system.Similarly, if TCP port 22 is open, then the device is likely executing aUNIX® operating system, such as LINUX®. If UDP port 161 is open, thenthe device may be able to be further identified through the SimpleNetwork Management Protocol (SNMP). Other possibilities exist. Once thepresence of a device at a particular IP address and its open ports havebeen discovered, these configuration items are saved in CMDB 500.

In the classification phase, proxy servers 312 may further probe eachdiscovered device to determine the version of its operating system. Theprobes used for a particular device are based on information gatheredabout the devices during the scanning phase. For example, if a device isfound with TCP port 22 open, a set of UNIX®-specific probes may be used.Likewise, if a device is found with TCP port 135 open, a set ofWINDOWS®-specific probes may be used. For either case, an appropriateset of tasks may be placed in task list 502 for proxy servers 312 tocarry out. These tasks may result in proxy servers 312 logging on, orotherwise accessing information from the particular device. Forinstance, if TCP port 22 is open, proxy servers 312 may be instructed toinitiate a Secure Shell (SSH) connection to the particular device andobtain information about the operating system thereon from particularlocations in the file system. Based on this information, the operatingsystem may be determined. As an example, a UNIX® device with TCP port 22open may be classified as AIX®, HPUX, LINUX®, MACOS®, or SOLARIS®. Thisclassification information may be stored as one or more configurationitems in CMDB 500.

In the identification phase, proxy servers 312 may determine specificdetails about a classified device. The probes used during this phase maybe based on information gathered about the particular devices during theclassification phase. For example, if a device was classified as LINUX®,a set of LINUX®-specific probes may be used. Likewise if a device wasclassified as WINDOWS® 2012, as a set of WINDOWS®-2012-specific probesmay be used. As was the case for the classification phase, anappropriate set of tasks may be placed in task list 502 for proxyservers 312 to carry out. These tasks may result in proxy servers 312reading information from the particular device, such as basicinput/output system (BIOS) information, serial numbers, networkinterface information, media access control address(es) assigned tothese network interface(s), IP address(es) used by the particular deviceand so on. This identification information may be stored as one or moreconfiguration items in CMDB 500.

In the exploration phase, proxy servers 312 may determine furtherdetails about the operational state of a classified device. The probesused during this phase may be based on information gathered about theparticular devices during the classification phase and/or theidentification phase. Again, an appropriate set of tasks may be placedin task list 502 for proxy servers 312 to carry out. These tasks mayresult in proxy servers 312 reading additional information from theparticular device, such as processor information, memory information,lists of running processes (applications), and so on. Once more, thediscovered information may be stored as one or more configuration itemsin CMDB 500.

Running discovery on a network device, such as a router, may utilizeSNMP. Instead of or in addition to determining a list of runningprocesses or other application-related information, discovery maydetermine additional subnets known to the router and the operationalstate of the router's network interfaces (e.g., active, inactive, queuelength, number of packets dropped, etc.). The IP addresses of theadditional subnets may be candidates for further discovery procedures.Thus, discovery may progress iteratively or recursively.

Once discovery completes, a snapshot representation of each discovereddevice, application, and service is available in CMDB 500. For example,after discovery, operating system version, hardware configuration andnetwork configuration details for client devices, server devices, androuters in managed network 300, as well as applications executingthereon, may be stored. This collected information may be presented to auser in various ways to allow the user to view the hardware compositionand operational status of devices, as well as the characteristics ofservices that span multiple devices and applications.

Furthermore, CMDB 500 may include entries regarding dependencies andrelationships between configuration items. More specifically, anapplication that is executing on a particular server device, as well asthe services that rely on this application, may be represented as suchin CMDB 500. For instance, suppose that a database application isexecuting on a server device, and that this database application is usedby a new employee onboarding service as well as a payroll service. Thus,if the server device is taken out of operation for maintenance, it isclear that the employee onboarding service and payroll service will beimpacted. Likewise, the dependencies and relationships betweenconfiguration items may be able to represent the services impacted whena particular router fails.

In general, dependencies and relationships between configuration itemsbe displayed on a web-based interface and represented in a hierarchicalfashion. Thus, adding, changing, or removing such dependencies andrelationships may be accomplished by way of this interface.

Furthermore, users from managed network 300 may develop workflows thatallow certain coordinated activities to take place across multiplediscovered devices. For instance, an IT workflow might allow the user tochange the common administrator password to all discovered LINUX®devices in single operation.

In order for discovery to take place in the manner described above,proxy servers 312, CMDB 500, and/or one or more credential stores may beconfigured with credentials for one or more of the devices to bediscovered. Credentials may include any type of information needed inorder to access the devices. These may include userid/password pairs,certificates, and so on. In some embodiments, these credentials may bestored in encrypted fields of CMDB 500. Proxy servers 312 may containthe decryption key for the credentials so that proxy servers 312 can usethese credentials to log on to or otherwise access devices beingdiscovered.

The discovery process is depicted as a flow chart in FIG. 5B. At block520, the task list in the computational instance is populated, forinstance, with a range of IP addresses. At block 522, the scanning phasetakes place. Thus, the proxy servers probe the IP addresses for devicesusing these IP addresses, and attempt to determine the operating systemsthat are executing on these devices. At block 524, the classificationphase takes place. The proxy servers attempt to determine the operatingsystem version of the discovered devices. At block 526, theidentification phase takes place. The proxy servers attempt to determinethe hardware and/or software configuration of the discovered devices. Atblock 528, the exploration phase takes place. The proxy servers attemptto determine the operational state and applications executing on thediscovered devices. At block 530, further editing of the configurationitems representing the discovered devices and applications may takeplace. This editing may be automated and/or manual in nature.

The blocks represented in FIG. 5B are for purpose of example. Discoverymay be a highly configurable procedure that can have more or fewerphases, and the operations of each phase may vary. In some cases, one ormore phases may be customized, or may otherwise deviate from theexemplary descriptions above.

V. Improved Machine Learning Classification

Generally, machine learning relates to the ability of computers to learnfrom and make predictions based on data. In practice, machine learningmay include a process of providing a machine learning algorithm withtraining data to learn from, so as to create a machine learning modelfrom this training data. The training data may include input valuesmapped to ground truth output values. Once the model is trained, it canpredict the output values for new input values.

As described above, a classifier is a particular type of machinelearning model that classifies observations (input values) into one ormore of a number of categories (output values). A classifier can betrained by providing a training data set including observations mappedto ground truth categories thereof. With enough training data, theclassifier can be expected to make reasonably accurate predictions ofthe categories of new observations.

Examples of machine learning classifiers include Bayesian classifiers,support vector machines, linear classifiers, k-nearest-neighborclassifiers, decision trees, random forests, and neural networks. Othertypes of classifiers may be possible.

A. Example Use of a Machine Learning Classifier

For purpose of example, suppose that an enterprise's IT departmentemploys a classifier to help it determine the categories of IT help desktrouble tickets. These tickets may be opened by technology users who arehaving difficulties with hardware, software, or networking services.Each ticket may include a short text field (e.g., 1000 characters orless) containing a description of the problem in the words of the userwho opened the ticket.

A challenge for a large enterprise is to properly prioritize opentickets into categories. Certain types of categories, such assecurity-related problems or wide-spread service outages are likely tobe more important for the enterprise to address quickly than problemsrelated to setting up an email account or deciding which web browser touse. Furthermore, users cannot be relied upon to self-categorize theirproblems reliably. What looks like a certain category of problem to auser might actually be of a different category. Or, a user may have noidea to which category his or her problem belongs, and therefore maydecline to specify any category.

An IT department may receive hundreds or thousands of tickets per day.Thus, it is desirable for these tickets to be categorized rapidly andaccurately. One way of addressing this problem to is employ a machinelearning classifier.

In order to make the examples herein tractable and illustrative, thefollowing assumes that there are only two categories in which the textfields of trouble tickets can be classified: email and VPN. In reality,there may be a number of additional categories for trouble tickets, suchas booting, web browsing, group chat, Internet access, mobile access,and so on. The concepts behind the simple binary classifier used hereincan easily be extended to any number of categories.

TABLE 1 Ground Truth Text Field Category “My email signature does nothave the company logo.” Email “Outgoing messages not working.” Email“Forgot my secure access password.” VPN “Can't add members todepartment's mailing list.” Email “VPN dropping every few minutes.” VPN“Getting authentication failure when trying to VPN access work fromhome.” “Two factor authentication not working.” VPN “Messages stuck inoutbox.” Email “Slow data rates when logged into corporate network.” VPN“VPN client crashing.” VPN

Table 1 provides examples of text fields from trouble ticketscategorized as either email issues or VPN issues. Here, it is assumedthat the ground truth categories are known. These ground truthcategories can be manually entered by IT professionals either from areading of the text fields, after each trouble ticket has been resolvedand its root cause has been identified, or in some other fashion.

In any event, a large data set similar to that of Table 1 (e.g., withhundreds or thousands of observations) may be gathered. Since the dataset maps input values to ground truth output values, it can be used totrain a classifier. This training may take place in a number of ways andin accordance with a number of algorithms. For instance, and withoutgetting into the finer details of training, a classifier may be trainedto look for certain keywords or keyphrases that are indicative of eitheremail or VPN issues. For example, the keywords “email”, “outbox”,“messages”, and “mailing” may be indicative of email issues, while thekeywords “password”, “authentication”, “VPN”, and “network” may beindicative of VPN issues. Nonetheless, more sophisticated categorizationtechniques may be learned by a classifier.

Regardless of how it is trained, it can be assumed that such aclassifier exists. One way of gaining insight into the performance of aclassifier is to examine its performance on new observations for whichthe ground truth classification is known. This can be accomplished bysplitting a training data set into two parts. One part (e.g., 80%) canbe used for the actual training, while the other part (e.g., theremaining 20%) can be used for evaluation. Since the part used forevaluation includes ground truth output values, the expected futureperformance of the classifier can be determined.

It may be implied that classifiers output just one predicted categoryfor each new observation. However, this need not be the case. Someclassifiers may instead output a probability that the new observationfalls into each of the possible categories. As an example of the latter,the trouble ticket classifier may output respective predictedprobabilities that each text field relates to email issues and VPNissues.

TABLE 2 Text Fields Email Probability VPN Probability Text field 1 60%40% Text field 2 10% 90% Text field 3 73% 27%

Table 2 illustrates a simple example of the output of such a classifier.For the observation “text field 1”, the classifier predicts that thetrouble ticket is related to email issues with 60% probability and toVPN issues with 40% probability. Similarly, for the observation “textfield 2”, the classifier predicts that the trouble ticket is related toemail issues with 10% probability and to VPN issues with 90%probability. Likewise, for the observation “text field 3”, theclassifier predicts that the trouble ticket is related to email issueswith 73% probability and to VPN issues with 27% probability.

From Table 2, it is clear that some predictions have a higher confidencethan others. For instance, the prediction associated with “text field 2”gives a 90% probability of being related to VPN issues, whereas theprediction associated with “text field 1” gives email issues and VPNissues rather similar probabilities—60% and 40%, respectively.

B. Improved Machine Learning Classifiers with Confidence Thresholds

It may be beneficial to be able to state whether a particular prediction(with associated probabilities for each category) meets a givenconfidence threshold. The confidence threshold can be between 0 and 1,inclusive. A prediction meets a particular confidence threshold if thehighest probability for any category is at least the confidencethreshold. As an example, suppose that the confidence threshold for eachcategory is 65% (0.65). Of the text fields in Table 2, the predictionsfor “text field 2” and “text field 3” meet the confidence threshold,because their highest probabilities are 90% (0.90) and 73% (0.73),respectively. On the other hand, the prediction for “text field 1” doesnot meet the confidence threshold because its highest probability isonly 60% (0.60).

A confidence threshold of 0% (0.00) is met by any prediction. Therefore,this confidence threshold essentially indicates that the confidenceassociated with predictions is irrelevant. A confidence threshold of100% (1.00) is only met by predictions that are absolutely certain towhich category an observation belongs. In practice, very few predictionswill meet such a high confidence threshold.

Note that the “confidence threshold” discussed herein is not necessarilyrelated to a confidence interval or any other statistical measure.Instead, it is a threshold degree of certainty to which predictions canbe compared. For any given confidence threshold, a particular predictionwill either have a probability with at least the confidence threshold,or this probability will be less than the confidence threshold.

When a prediction does not meet a confidence threshold, that predictionmay be ignored. In some cases, this is appropriate because theprediction has little value to the end user. For instance, if thetrouble ticket classifier predicts that a certain text field value isrelated to email issues with 50% probability and related to VPN issueswith 50% probability, this outcome is virtually useless to an ITprofessional. As such, predictions that do not meet the selectedconfidence threshold can be considered “don't know” predictions. Thepresence of such “don't know” predictions can be used, along with thepredictions that meet the selected confidence threshold, to determinethe effectiveness of the classifier. These “don't know” predictions maybe referred to as being of a null category (i.e., a category other thanone of the categories in which the observations are initiallyclassified).

TABLE 3 Predicted Email Predicted VPN Don't Know Total Actual Email 15023 7 180 Actual VPN 20 292 125 437 Total 170 315 132

Table 3 is a matrix providing hypothetical output of the trouble ticketclassifier on 617 new observations, where a particular confidencethreshold was used (for sake of this example, the actual value of thisconfidence threshold does not matter). Out of these, 180 were actuallyrelated to email issues, and 437 were actually related to VPN issues.The classifier predicted that 170 were related to email issues and that315 were related to VPN issues. The classifier considered 132observations as “don't know” because those observations did not meet theconfidence threshold.

From this matrix, a few metrics can be derived. Each category has aprecision value and a coverage value. The precision value is thepercentage of observations, out of all observations predicted to be in aparticular category, that are actually in the particular category. Thecoverage value is the percentage of non-null-category predictions madefor observations in a particular category out of the total number ofobservations that are actually in the particular category.

Applying these metrics to the data in Table 3, email precision is150/170=88.2%, email coverage is (150+23)/180=96.1%, VPN precision is292/315=92.7%, and VPN coverage is (20+292)/437=71.4%. Interpretingthese metrics, one can conclude that trouble tickets predicted to berelated to email issues are correct 88.2% of the time, and 96.1% ofthese trouble tickets will result in usable prediction. One can alsoconclude that trouble tickets predicted to be related to VPN issues arecorrect 92.7% of the time, and 71.4% of these trouble tickets willresult in usable prediction.

In an ideal situation, both precision and coverage will be close to 100%for all categories of a classifier. In practice, however, this is rarelythe case. The inherent noise in real-world data sets often preventseither precision, coverage, or both from being that high.

Nonetheless, for some applications, the end user may find it desirableto have a very high precision for one or more categories at the expenseof coverage, or vice versa. As an example, for the trouble ticketclassifier, IT professionals may wish to have a very high precision forVPN related trouble tickets. This may be the situation because VPNissues are fundamentally related to enterprise security, and should beaddressed expeditiously. Therefore, the IT professionals might not wantVPN issues to be miscategorized as email issues, because email issuescould be given a lower priority and critical VPN issues that aremisclassified as email issues might be ignored for hours. Further, theIT professionals may be willing to manually examine a higher number of“don't know” trouble tickets (indicative of low coverage) in order toachieve this high precision. On the other hand, the same ITprofessionals may be willing to accept a lower precision for emailissues as long as most trouble tickets relating to actual email issuesare categorized as something other than “don't know.”

These factors suggest that there may be a necessary tradeoff betweenprecision and coverage for each of the classifier's categories, and thatthis tradeoff is controlled by the selected confidence threshold. Thus,it may be helpful for a machine learning classifier (or relatedsoftware) to be able to (i) suggest a “good” confidence threshold thatprovides both a reasonably high precision and a reasonably highcoverage, and (ii) provide a mechanism through which the end user canselect a confidence threshold from a number of candidate confidencethresholds, where this selection provides the end user with anacceptable tradeoff between precision and coverage.

TABLE 4 Confidence VPN VPN Objective Threshold Precision CoverageFunction 0.5 52.4%  100% 0.524 0.6 81.3% 74.9% 0.609 0.7 92.7% 71.4%0.662 0.8 95.4% 66.1% 0.630 0.9 98.6% 64.5% 0.636

Table 4 provides examples of VPN precision and VPN coverage for variousconfidence thresholds. For instance, a confidence threshold of 0.6results in a VPN precision of 81.3% and a VPN coverage of 74.9%.

Table 4 also provides an “Objective Function” column that contains themathematical product of the precision and the coverage for eachconfidence threshold. As an example, the confidence threshold of 0.6 hasan objective function value of (0.813)(0.749)=0.609. Since the value ofthe objective function value scales with both precision and coverage, itcan be used to evaluate which confidence threshold provides a goodtradeoff between precision and coverage. Particularly, the higher theproduct, the better the confidence threshold. In Table 4, the confidencethreshold of 0.7 has the highest objective function value of 0.662. Insome embodiments an objective function other than a product can be used,such as a sum of the precision and coverage or a linear combination ofthese values.

Also, in this example, the confidence threshold of 0.5 provides acoverage of 100%. This is due to the example being a binaryclassifier—thus, at least one of the two possible categories will have aprobability of at least 50%. This implies that there is no need toconsider confidence thresholds of less than 0.5, as the precision canonly decrease while the coverage cannot increase. When classifiers withmore than two categories are used, confidence thresholds lower than 0.5can be used without experiencing this limitation.

As an illustration of the tradeoff between precision and coverage, FIG.6A plots the precision and coverage values from Table 4 against eachother on graph 600. Notably, there is a roughly inverse linearrelationship between precision and coverage for the example data. Also,the point for a precision of 52.4% is placed lower than 100% on thecoverage axis due to space limitations, but its position as shown stillserves to represent this relationship.

As an illustration of the relationship between confidence threshold andthe objective function, FIG. 6B plots these values from Table 4 againsteach other on graph 602. As noted previously, the maximum objectivefunction is associated with a confidence threshold of 0.7.

FIG. 7 provides an example of how a machine learning classifier can betrained, and example output that it could produce. Particularly,training data 700 is a data set that maps observations to their groundtruth categories. In this case, the trouble ticket example discussedherein is used, so training data 700 would map text fields to thecategories of email and/or VPN.

From training data 700, machine learning classifier 702 is built. Thisclassifier attempts to predict, for new observations, the probabilitieswith which these observations will be of each category.

Once machine learning classifier 702 is built, it can be tested withtest data 704. Test data 704 may also map observations to their groundtruth categories, e.g., text fields to the categories of email or VPN.In fact, training data 700 and test data 704 may both be derived fromthe same master data set. For instance, the master data set may havebeen split into two parts, one containing 80% of the master data set andthe other containing 20% of the master data set. The part with 80% ofthe master data set may be training data 700, and the part with 20% ofthe master data set may be test data 704.

Applying machine learning classifier 702 to test data 704 may result inoutput 706. This output provides, for each observation, the predictedprobabilities that the observation belongs to each of the categories.While output 706 shows the ground truth category for each observation,these ground truth values may be obtained directly from test data 704rather than through machine learning classifier 702.

In this example, each observation is a text field, and the twocategories are email and VPN. In each entry of output 706, machinelearning classifier 702 provides probabilities for each category. Butthe category with the highest probability for each entry may beconsidered to be the predicted category. Thus, text field 1 isclassified as email, text field 2 is classified as email, text field 3is classified as VPN, and so on.

In output 706 there are four entries that belong to the email category,two of which were predicted correctly. Thus, the email category has aprecision of 50%. There are also six entries that belong to the VPNcategory, four of which were predicted correctly. Thus, the VPN categoryhas a precision of 66.6%. Since there are no “don't know” predictions,both categories have a coverage of 100%.

In order to determine the impact of confidence thresholds on precisionand coverage, each confidence threshold may be applied, one by one, tooutput 706. For instance, applying a confidence threshold of 0.6 tooutput 706 results in text fields 4 and 6 becoming “don't know”predictions, because their highest respective probabilities are lessthan 0.6. A matrix similar to that of Table 3 can be formed for thisconfidence threshold, and is shown as Table 5.

TABLE 5 Predicted Email Predicted VPN Don't Know Total Actual Email 2 11 4 Actual VPN 1 4 1 6 Total 3 5 2

From Table 5, email precision is 66.7%, email coverage is 75.0%, VPNprecision is 80.0%, and VPN coverage is 83.3%. The value of theobjective function for email is 0.5 and for VPN is 0.67.

Likewise, applying a confidence threshold of 0.8 to output 706 resultsin text fields 1, 3, 4, 5, 6, and 10 becoming “don't know” predictions,because their highest respective probabilities are less than 0.8. Amatrix similar to that of Table 3 can be formed for this confidencethreshold, and is shown as Table 6.

TABLE 6 Predicted Email Predicted VPN Don't Know Total Actual Email 1 12 4 Actual VPN 0 2 4 6 Total 1 3 6

From Table 6, email precision is 100.0%, email coverage is 50.0%, VPNprecision is 66.6%, and VPN coverage is 33.3%. The value of theobjective function for email is 0.5 and for VPN is 0.22.

In this simple example, the impact of each applied confidence thresholdcan be determined. For email, using a confidence threshold of 0.8instead of 0.6 results in no change to the objective function value. ForVPN, using a confidence threshold of 0.8 instead of 0.6 results in alower objective function value. This suggests that the end user use aconfidence threshold of 0.6 for VPN, and that either confidencethreshold may be used for email. Thus, this procedure can result indifferent confidence thresholds being recommended for differentcategories.

The examples provided herein are simple for purpose of illustration.Actual embodiments of training data 700 and test data 704 may includehundreds or thousands of observations mapped to respective categories.Additionally, machine learning classifier 702 may be a multi-categoryclassifier supporting more than just two categories. As notedpreviously, while each new observation may be classified into a singleone of these categories, machine learning classifier 702 may provide aprobability that an observation belongs to each category, where thecategory with the highest probability is the category in which theobservation is classified.

C. Example Graphical User Interfaces

Based on the discussion above, various techniques may be employed toimprove machine learning classifier efficacy when confidence thresholdsare used. The machine learning classifier may be applied to input datawith ground truth categories for each observation therein, and produceoutput (such as output 706). This output is then filtered (i.e.,reclassified) by each of some number of confidence thresholds. For eachconfidence threshold, a precision value, coverage value, and objectivefunction value may be determined. For each category, the highestobjective function value across all confidence thresholds may beselected as the recommend default for that category. But the end usermay be able to select a confidence threshold with a lower objectivefunction value through use of a graphical user interface.

FIG. 8A depicts a graphical user interface 800 containing a table. Thetable includes entries for each of at least five categories in whichtrouble tickets can be classified (for purpose of illustration, thisexample expands upon the binary classifier described above). Each entryis represented by a row of the table, and contains the category name,estimated precision, estimated coverage, and top keywords associatedwith observations that were classified into the category. The estimatedprecision and estimated coverage values shown may be associated withvarious default or user-selected confidence thresholds for eachcategory. Thus, observations in these categories may be reclassified inaccordance with the category's confidence threshold in order to separateout observations that the machine learning classifier cannot classifywith a high enough confidence. The table may be able to be sorted by anyof the columns if the end user clicks on or otherwise activates the nameof the column.

Graphical user interface 800 represents a configuration associated witha machine learning classifier. For example, when applied to furtherobservations, the machine learning classifier is expected to exhibit a60% precision and a 98% coverage when classifying email.

If the end user selects or otherwise activates one of the informationicons 804, the graphical user interface may display more informationabout the associated category. If the end user selects or otherwiseactivates one of the checkboxes 802, the end user may be taken toanother graphical user interface that provides estimated precision andcoverage values for the associated category over a number of confidencethresholds.

To that point, in FIG. 8B, graphical user interface 806 shows a tablethat contains estimated precision and coverage values for the VPNcategory across a number of confidence thresholds. The defaultconfidence threshold, as determined by the objective function, is in thesecond to last row, and is italicized. This row contains the sameprecision and coverage values as the VPN entry in the table of FIG. 8A.But precision and coverage values for a range of other confidencethresholds are also displayed. The table may be able to be sorted by anyof the columns if the end user clicks on or otherwise activates the nameof the column.

If the end user selects or otherwise activates one of the informationicons 810, the graphical user interface may display more informationabout the associated confidence threshold. If the end user selects orotherwise activates one of the checkboxes 808, the associated confidencethreshold may be selected. Then the end user may be returned tographical user interface 800, which displays precision and coveragevalues for the selected confidence threshold. In this way, the user canoverride the default precision and coverage values as he or she seesfit.

There are at least three ways in which this technique improves overtraditional machine learning classifiers. First, confidence thresholdscan be taken into account, which allows the end user to set aside(reclassify to a null category) observations that the machine learningclassifier does not classify with a corresponding degree of certainty.Doing so gives the end user more control over the classificationprocess. Second, the objective function provides a recommendedconfidence threshold to the end user by default, where thisrecommendation may be a reasonable tradeoff between precision andcoverage. Third, the end user can view pairs of precision and coveragevalues associated with a number of confidence thresholds. Based on theend user's goals and judgment, he or she can override the defaultconfidence threshold that was determined using the objective function,and select a different pair of precision and coverage values. Additionalimprovements are possible.

VI. Example Operations

FIG. 9 is a flow chart illustrating an example embodiment. The processillustrated by FIG. 9 may be carried out by a computing device, such ascomputing device 100, and/or a cluster of computing devices, such asserver cluster 200. However, the process can be carried out by othertypes of devices or device subsystems. For example, the process could becarried out by a portable computer, such as a laptop or a tablet device.

The embodiments of FIG. 9 may be simplified by the removal of any one ormore of the features shown therein. Further, these embodiments may becombined with features, aspects, and/or implementations of any of theprevious figures or otherwise described herein.

Block 900 may involve receiving, by a machine learning classifier thatclassifies observations into one or more of i categories, test data. Thetest data may include j observations, each associated with a respectiveground truth category. The ground truth categories may be from the icategories. Both i and j may take on values of 1 or more.

Block 902 may involve producing, by the machine learning classifier,output that provides, for each particular observation of the jobservations, a set of i probabilities, one probability for each of thei categories.

Block 904 may involve obtaining, by a computing device, k confidencethresholds. The value of k may also be 1 or more.

Block 906 may involve, for each particular confidence threshold in the kconfidence thresholds, the computing device: reclassifying, into a nullcategory that is not one of the i categories, any of the j observationsfor which all of the set of i probabilities are less than the particularconfidence threshold, and determining, based on the j observations afterreclassification and their associated sets of i probabilities, arespective precision value for a particular category of the i categoriesand a respective coverage value for the particular category.

Block 908 may involve, possibly based on the k respective precisionvalues and the k respective coverage values, selecting a specificconfidence threshold in the k confidence thresholds.

Block 910 may involve reclassifying, into the null category inaccordance with the specific confidence threshold, at least some furtherobservations in further output from the machine learning classifier.

Block 912 may involve providing the reclassified further observationswith the further output.

In some embodiments, the machine learning classifier predicts that aspecific observation of the j observations belongs to the particularcategory when the particular category is associated with a highest ofthe set of i probabilities for the specific observation.

In some embodiments, the respective precision value indicates anaccuracy with which the machine learning classifier predicts that the jobservations are in their associated ground truth categories.

In some embodiments, the respective coverage value indicates, for theparticular category, the percentage of the j observations that were notreclassified into the null category.

In some embodiments, further operations may include: for each particularconfidence threshold in the k confidence thresholds, calculate arespective objective function value based on the respective precisionand the respective coverage. Selecting the specific confidence thresholdmay involve selecting an objective function value from the k respectiveobjective function values. The selected objective function value may beassociated with the specific confidence threshold.

In some embodiments, the respective objective function value is amultiplicative product of the respective precision value and therespective coverage value. Alternatively or additionally, selecting theobjective function value from the k respective objective function valuesinvolves selecting a highest objective function value of the krespective objective function values.

In some embodiments, selecting the specific confidence threshold mayinvolve: transmitting, to a client device, a table-based graphicalrepresentation of the k respective precision values and the k respectivecoverage values, where each row in the table depicts an associated pairof the k respective precision values and the k respective coveragevalues, and receiving, from the client device, a selection of one of therows in the table, where the selected row contains a precision value anda coverage value associated with the specific confidence threshold.

In some embodiments, each of the further observations are associatedwith respective sets of i probabilities, one probability for each of thei categories. Reclassifying at least some further observations mayinvolve reclassifying any of the further observations for which all ofthe associated set of i probabilities are less than the specificconfidence threshold.

In some embodiments, the set of i probabilities for the particularobservation are predicted likelihoods that the particular observationbelongs to each of the i categories, respectively.

In some embodiments, the confidence threshold is between 0 and 1,inclusive.

VII. Conclusion

The present disclosure is not to be limited in terms of the particularembodiments described in this application, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its scope, as will be apparent to thoseskilled in the art. Functionally equivalent methods and apparatuseswithin the scope of the disclosure, in addition to those describedherein, will be apparent to those skilled in the art from the foregoingdescriptions. Such modifications and variations are intended to fallwithin the scope of the appended claims.

The above detailed description describes various features and operationsof the disclosed systems, devices, and methods with reference to theaccompanying figures. The example embodiments described herein and inthe figures are not meant to be limiting. Other embodiments can beutilized, and other changes can be made, without departing from thescope of the subject matter presented herein. It will be readilyunderstood that the aspects of the present disclosure, as generallydescribed herein, and illustrated in the figures, can be arranged,substituted, combined, separated, and designed in a wide variety ofdifferent configurations.

With respect to any or all of the message flow diagrams, scenarios, andflow charts in the figures and as discussed herein, each step, block,and/or communication can represent a processing of information and/or atransmission of information in accordance with example embodiments.Alternative embodiments are included within the scope of these exampleembodiments. In these alternative embodiments, for example, operationsdescribed as steps, blocks, transmissions, communications, requests,responses, and/or messages can be executed out of order from that shownor discussed, including substantially concurrently or in reverse order,depending on the functionality involved. Further, more or fewer blocksand/or operations can be used with any of the message flow diagrams,scenarios, and flow charts discussed herein, and these message flowdiagrams, scenarios, and flow charts can be combined with one another,in part or in whole.

A step or block that represents a processing of information cancorrespond to circuitry that can be configured to perform the specificlogical functions of a herein-described method or technique.Alternatively or additionally, a step or block that represents aprocessing of information can correspond to a module, a segment, or aportion of program code (including related data). The program code caninclude one or more instructions executable by a processor forimplementing specific logical operations or actions in the method ortechnique. The program code and/or related data can be stored on anytype of computer readable medium such as a storage device including RAM,a disk drive, a solid state drive, or another storage medium.

The computer readable medium can also include non-transitory computerreadable media such as computer readable media that store data for shortperiods of time like register memory and processor cache. The computerreadable media can further include non-transitory computer readablemedia that store program code and/or data for longer periods of time.Thus, the computer readable media may include secondary or persistentlong term storage, like ROM, optical or magnetic disks, solid statedrives, compact-disc read only memory (CD-ROM), for example. Thecomputer readable media can also be any other volatile or non-volatilestorage systems. A computer readable medium can be considered a computerreadable storage medium, for example, or a tangible storage device.

Moreover, a step or block that represents one or more informationtransmissions can correspond to information transmissions betweensoftware and/or hardware modules in the same physical device. However,other information transmissions can be between software modules and/orhardware modules in different physical devices.

The particular arrangements shown in the figures should not be viewed aslimiting. It should be understood that other embodiments can includemore or less of each element shown in a given figure. Further, some ofthe illustrated elements can be combined or omitted. Yet further, anexample embodiment can include elements that are not illustrated in thefigures.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purpose ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims.

What is claimed is:
 1. A computing system comprising: a machine learningclassifier that: receives input observations; predicts, for eachrespective input observation, a set of probabilities that indicate alikelihood of each respective input observation belonging to eachrespective output category of a plurality of output categories, whereinthe set of probabilities are predicted based at least in part on akeyword analysis of the input observations identify one or more keywordswithin test data, wherein each of the one or more keywords areassociated with information technology help desk trouble tickets; andclassifies each respective input observation into the output categoryhaving a highest probability; and a memory comprising programinstructions that, when executed by a processor, cause the computingsystem to: obtain a plurality of confidence thresholds for predictionsmade by the machine learning classifier; reclassify, for each confidencethreshold of the plurality of confidence thresholds, any of the inputobservations for which all probabilities of the set of probabilities areless than the confidence threshold into a null category that is not oneof the output categories; after the reclassifying, determine, for eachconfidence threshold of the plurality of confidence thresholds and foreach output category, a precision value that indicates an accuracy withwhich the machine learning classifier predicts that the inputobservations are in a respective ground truth category; after thereclassifying, determine, for each confidence threshold of the pluralityof confidence thresholds and for each output category, a coverage valuethat indicates a percentage of the input observations that were notreclassified into the null category; after the reclassifying, determine,for each confidence threshold of the plurality of confidence thresholdsand for each output category, an objective function value, wherein theobjective function value is a multiplicative product of the precisionvalue and the coverage value; generate, via the processor, a graphicaluser interface for display on a client device coupled to the processor,wherein the graphical user interface visually associates the pluralityof confidence thresholds, precision values, coverage values, results ofthe keyword analysis, and objective function values for each of theoutput categories, wherein the confidence threshold is one of theplurality of confidence thresholds displayed on the graphical userinterface, and wherein each of the plurality of confidence thresholdsare selectably displayed with the precision values, the coverage values,and the objective function values; receive a selection input via thegraphical user interface of one of the precision values, the coveragevalues, or the objective function values, or any combination thereof foreach of the previously presented output categories; and perform, inconjunction with the machine learning classifier, further classificationof additional input observations into the output categories and the nullcategory in accordance with a combination of the precision values, thecoverage values, or the objective function values, or any combinationthereof, via the graphical user interface.
 2. The computing system ofclaim 1, wherein a respective objective function value is displayed onthe graphical user interface for each of the plurality of confidencethresholds, and wherein a highest objective function value of theobjective function values is emphasized on the graphical user interface.3. The computing system of claim 1, wherein the graphical user interfacerepresents a table, each row in which depicts an associated set of theplurality of confidence thresholds, the precision values, and thecoverage values.
 4. The computing system of claim 1, wherein each of theadditional input observations are associated with an additionalrespective set of probabilities, and wherein classification of theadditional input observations into the output categories and the nullcategory comprises: reclassifying any of the additional inputobservations for which all of the additional respective set ofprobabilities are less than the confidence threshold into the nullcategory.
 5. The computing system of claim 1, wherein the confidencethreshold is between 0 and 1, inclusive.
 6. A method comprising:obtaining, by a computing system, a plurality of confidence thresholdsfor predictions made by a machine learning classifier, wherein themachine learning classifier: receives input observations; predicts, foreach respective input observation, a set of probabilities that indicatea likelihood of each respective input observation belonging to eachrespective output category of a plurality of output categories, whereinthe set of probabilities are predicted based at least in part on akeyword analysis of the input observations, and wherein each of one ormore keywords are associated with information technology help desktrouble tickets; and classifies each respective input observation intothe output category having a highest probability of the plurality ofoutput categories; reclassifying, for each confidence threshold of theplurality of confidence thresholds, by the computing system, any of theinput observations for which all probabilities of the set ofprobabilities are less than a corresponding confidence threshold into anull category that is not one of the output categories; after thereclassifying, determining, by the computing system, for each confidencethreshold of the plurality of confidence thresholds and for each of theplurality of output categories, a precision value that indicates anaccuracy with which the machine learning classifier predicts that theinput observations are in a respective ground truth categories; afterthe reclassifying, determining, by the computing system, for eachconfidence threshold of the plurality of confidence thresholds and foreach of the plurality of output categories, a coverage value thatindicates a percentage of the input observations that were notreclassified into the null category; after the reclassifying,determining, by the computing system, for each confidence threshold ofthe plurality of confidence thresholds and for each of the plurality ofoutput categories, an objective function value, wherein the objectivefunction value is a multiplicative product of the precision value andthe coverage value; generating a graphical user interface for display,wherein the graphical user interface visually associates the pluralityof confidence thresholds, precision values, coverage values, results ofthe keyword analysis, and objective function values for each of theplurality of output categories, wherein the confidence threshold is oneof the plurality of confidence thresholds on the graphical userinterface, and wherein each of the plurality of confidence thresholdsare selectably displayed with the precision values, the coverage values,and the objective function values; receiving a selection input via thegraphical user interface of one of the precision values, the coveragevalues, or the objective function values, or any combination thereof foreach of the plurality of output categories; and performing, by at leastthe machine learning classifier, further classification of additionalinput observations into the plurality of output categories and the nullcategory in accordance with a combination of the precision values, thecoverage values, or the objective function values, or any combinationthereof, via the graphical user interface.
 7. The method of claim 6,wherein a respective objective function value is displayed on thegraphical user interface for each of the plurality of confidencethresholds, and wherein a highest objective function value of theobjective function values is emphasized on the graphical user interface.8. The method of claim 6, wherein the graphical user interfacerepresents a table, each row in which depicts an associated set of theplurality of confidence thresholds, the precision values, and thecoverage values.
 9. The method of claim 6, wherein each of theadditional input observations are associated with a respective set ofprobabilities that the respective input observation belongs to each ofthe set of respective output categories, and wherein classification ofthe additional input observations into the set of respective outputcategories and the null category comprises: reclassifying, into the nullcategory, any of the additional input observations for which all of theprobabilities of the respective set of probabilities are less than thecorresponding confidence threshold into the null category.
 10. Themethod of claim 6, wherein the corresponding confidence threshold isbetween 0 and 1, inclusive.
 11. The method of claim 6, comprising:presenting on the graphical user interface a table that visuallyassociates each confidence threshold to a precision value, a coveragevalue, a result of the keyword analysis, and an objective function valuefor the confidence threshold.
 12. The method of claim 11, comprising:generating data corresponding to the table, wherein the data isconfigured to cause the table to comprise a column of the results of thekeyword analysis.
 13. The method of claim 11, comprising: generatingdata corresponding to the table, wherein the data is configured to causethe table to comprise a column of the precision values, a column of thecoverage values, and the column of the results of the keyword analysis.14. An article of manufacture including a non-transitorycomputer-readable medium, having stored thereon program instructionsthat, upon execution by a computing system, cause the computing systemto perform operations, the operations comprising: obtaining a pluralityof confidence thresholds for predictions made by a machine learningclassifier, wherein the machine learning classifier: receives inputobservations; predicts, for each respective input observation, a set ofprobabilities that indicate a likelihood of each respective inputobservation belonging to each respective output category of a pluralityof output categories, wherein the set of probabilities are predictedbased at least in part on a keyword analysis of the input observations,and wherein each of one or more keywords are associated with informationtechnology help desk trouble tickets; and classifies each respectiveobservation into the output category having a highest probability of theplurality of output categories; reclassifying, for each confidencethreshold of the plurality of confidence thresholds, any of the inputobservations for which all probabilities of the set of probabilities areless than the confidence threshold into a null category that is not oneof the plurality of output categories; after the reclassifying,determining, for each confidence threshold of the plurality ofconfidence thresholds and for each output category of the plurality ofoutput categories, a precision value that indicates an accuracy withwhich the machine learning classifier predicts that the inputobservations are in a respective ground truth categories; after thereclassifying, determining, for each confidence threshold of theplurality of confidence thresholds and for each output category of theplurality of output categories, a coverage value that indicates apercentage of the input observations that were not reclassified into thenull category; after the reclassifying, determining, for each confidencethreshold of the plurality of confidence thresholds and for each outputcategory of the plurality of output categories, an objective functionvalue, wherein the objective function value is a multiplicative productof the precision value and the coverage value; generating, a graphicaluser interface for display, wherein the graphical user interfacevisually associates the plurality of confidence thresholds, precisionvalues, coverage values, results of the keyword analysis, and objectivefunction values for each of the plurality of output categories, whereineach confidence threshold of the plurality of confidence thresholds ison the graphical user interface, and wherein each of the plurality ofconfidence thresholds are selectably displayed with the precisionvalues, the coverage values, and the objective function values;receiving a selection input via the graphical user interface of one ofthe precision values, the coverage values, or the objective functionvalues, or any combination thereof for each of the plurality of outputcategories; and performing, by at least the machine learning classifier,further classification of additional input observations into the outputcategories and the null category in accordance with a combination of theprecision values, the coverage values, or the objective function values,or any combination thereof via the graphical user interface.